US8145615B2 - Search and exploration using analytics reference model - Google Patents
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- visual items in order to convey or receive information, and in order to collaborate.
- visual items might include, for example, concept sketches, engineering drawings, explosions of bills of materials, three-dimensional models depicting various structures such as buildings or molecular structures, training materials, illustrated installation instructions, planning diagrams, and so on.
- CAD Computer Aided Design
- solid modeling applications allow authors to attach data and constraints to the geometry.
- the application for constructing a bill of materials might allow for attributes such as part number and supplier to be associated with each part, the maximum angle between two components, or the like.
- An application that constructs an electronic version of an arena might have a tool for specifying a minimum clearance between seats, and so on.
- any given application does have limits on the type information that can be visually conveyed, how that information is visually conveyed, or the scope of data and behavior that can be attributed to the various visual representations. If the application is to be modified to go beyond these limits, a new application would typically be authored by a computer programmer which expands the capabilities of the application, or provides an entirely new application. Also, there are limits to how much a user (other than the actual author of the model) can manipulate the model to test various scenarios.
- Embodiments described herein relate to searching using a data-driven analytics model.
- the analytics model includes an analytical modeling component that defines analytical relationships between model variables using a number of analytical relations. For example, such analytical relations might be equations, rules, constraints, simulations, or any other analytical relationship between model variables.
- the analytics model identifies which of the model variables is or are to be solved for, and which are input variable(s). In one embodiment, the identity of the output and input model variable(s) may change from one solve operation to the next.
- the output variable(s) of the solve operation are identified.
- the output variable(s) may have even been identified based on the search request.
- values for one or more of the input variable(s) may also be derived in preparation for the solve operation based on information provided in the search request.
- the analytical relations of the model may then be used to solve for the identified output variable(s).
- the resulting value(s) for the now solved-for output variable(s) may then be used to formulate the response to the search request.
- the nature of the response may vary depending on the scope of the application that embodied the search request capability.
- the results of the search request may be used for further exploration of the as model by, for example, submitting follow-up search requests.
- the follow-up search request may, perhaps, result in another solve operation using the analytical relations.
- the output variable(s) solved for in the next solve operation may be the same or different than the output variable(s) for the prior solve operation.
- the solved-for value for the prior search request may be used for the next search request.
- FIG. 1 illustrates an environment in which the principles of the present invention may be employed including a data-driven composition framework that constructs a view composition that depends on input data;
- FIG. 2 illustrates a pipeline environment that represents one example of the environment of FIG. 1 ;
- FIG. 3 schematically illustrates an embodiment of the data portion of the pipeline of FIG. 2 ;
- FIG. 4 schematically illustrates an embodiment of the analytics portion of the pipeline of FIG. 2 ;
- FIG. 5 schematically illustrates an embodiment of the view portion of the pipeline of FIG. 2 ;
- FIG. 6 illustrates a rendering of a view composition that may be constructed by the pipeline of FIG. 2 ;
- FIG. 7 illustrates a flowchart of a method for generating a view composition using the pipeline environment of FIG. 2 ;
- FIG. 8 illustrates a flowchart of a method for regenerating a view composition in response to user interaction with the view composition using the pipeline environment of FIG. 2 ;
- FIG. 9 schematically illustrates the solver of the analytics portion of FIG. 4 in further detail including a collection of specialized solvers
- FIG. 10 illustrates a flowchart of the solver of FIG. 9 solving for unknown model parameters by coordinating the actions of a collection of specialized solvers;
- FIG. 11 schematically illustrates a solver environment that represents an example of the solver of FIG. 9 ;
- FIG. 12 illustrates a flowchart of a method for using the solver environment of FIG. 11 to solve for model analytics
- FIG. 13 illustrates a rendering of an integrated view composition that extends the example of FIG. 6 ;
- FIG. 14 illustrates a visualization of a shelf layout and represents just one of countless applications that the principles described herein may apply to;
- FIG. 15 illustrates a visualization of an urban plan that the principles described herein may also apply to
- FIG. 16 illustrates a conventional visualization comparing children's education, that the principles of the present invention may apply to thereby creating a more dynamic learning environment
- FIG. 17 illustrates a conventional visualization comparing population density, that the principles of the present invention may apply to thereby creating a more dynamic learning environment
- FIG. 18 illustrates a taxonomy environment in which the taxonomy component of FIG. 2 may operate
- FIG. 19 illustrates an example of a taxonomy of the member items of FIG. 18 ;
- FIGS. 20A through 20C show three examples of taxonomies of related categories
- FIG. 21 illustrates a member item that includes multiple properties
- FIG. 22 illustrates a domain-specific taxonomy and represents one example of the domain-specified taxonomies of FIG. 18 ;
- FIG. 23 illustrates a flowchart of a method for navigating and using analytics
- FIG. 24 illustrates a flowchart of a method for searching using analytics
- FIG. 25 illustrates a computing system that represents an environment in which the composition framework of FIG. 1 (or portions thereof) may be implemented.
- FIG. 1 illustrates a visual composition environment 100 that uses data-driven analytics and visualization of the analytical results.
- the environment 100 also called hereinafter a “pipeline”
- the environment 100 includes a composition framework 110 that performs logic that is performed independent of the problem-domain of the view construction 130 .
- the same composition framework 110 may be used to compose interactive view compositions for city plans, molecular models, grocery shelf layouts, machine performance or assembly analysis, or other domain-specific renderings.
- the analytics may be used to search and explore in various scenarios. First, however, the basic composition framework 110 will be described in detail.
- the composition framework 110 uses domain-specific data 120 that is taxonomically organized in a domain-specific way to construct the actual visual construction 130 that is specific to the domain. Accordingly, the same composition framework 110 may be used to construct view compositions for any number of different domains by changing the domain-specific data 120 , rather than having to recode the composition framework 110 itself. Thus, the composition framework 110 of the pipeline 100 may apply to a potentially unlimited number of problem domains, or at least to a wide variety of problem domains, by altering data, rather than recoding and recompiling.
- the view construction 130 may then be supplied as instructions to an appropriate 2-D or 3-D rendering module.
- the architecture described herein also allows for convenient incorporation of pre-existing view compositions as building blocks to new view compositions. In one embodiment, multiple view compositions may be included in an integrated view composition to allow for easy comparison between two possible solutions to a model.
- FIG. 2 illustrates an example architecture of the composition framework 110 in the form of a pipeline environment 200 .
- the pipeline environment 200 includes, amongst other things, the pipeline 201 itself
- the pipeline 201 includes a data portion 210 , an analytics portion 220 , and a view portion 230 , which will each be described in detail with respect to subsequent FIGS. 3 through 5 , respectively, and the accompanying description.
- the data portion 210 of the pipeline 201 may accept a variety of different types of data and presents that data in a canonical form to the analytics portion 220 of the pipeline 201 .
- the analytics portion 220 binds the data to various model parameters, and solves for the unknowns in the model parameters using model analytics.
- the various parameter values are then provided to the view portion 230 , which constructs the composite view using those values of the model parameters.
- the pipeline environment 200 also includes an authoring component 240 that allows an author or other user of the pipeline 201 to formulate and/or select data to provide to the pipeline 201 .
- the authoring component 240 may be used to supply data to each of data portion 210 (represented by input data 211 ), analytics portion 220 (represented by analytics data 221 ), and view portion 230 (represented by view data 231 ).
- the various data 211 , 221 and 231 represent an example of the domain-specific data 120 of FIG. 1 , and will be described in much further detail hereinafter.
- the authoring component 240 supports the providing of a wide variety of data including for example, data schemas, actual data to be used by the model, the location or range of possible locations of data that is to be brought in from external sources, visual (graphical or animation) objects, user interface interactions that can be performed on a visual, modeling statements (e.g., views, equations, constraints), bindings, and so forth.
- data schemas for example, data schemas, actual data to be used by the model, the location or range of possible locations of data that is to be brought in from external sources, visual (graphical or animation) objects, user interface interactions that can be performed on a visual, modeling statements (e.g., views, equations, constraints), bindings, and so forth.
- the authoring component is but one portion of the functionality provided by an overall manager component (not shown in FIG. 2 , but represented by the composition framework 110 of FIG. 1 ).
- the manager is an overall director that controls and sequences the operation of all the other components (such as data connectors, solvers, viewers, and so forth) in response to events (such as user interaction events, external data events, and events from any of the other components such as the solvers, the operating system, and so forth).
- the authoring component 240 also includes a search tool 242 that allows for searches to be performed.
- the search tool 242 is able to draw on the data-driven analytical capabilities of the pipeline 201 in order to perform complex search operations. For instance, in some cases, one or more parameters of the search might first need to be solved for in order to complete the search.
- the data driven analytics is a city map
- some of the analytics are capable of solving for the typical noise level at particular coordinates in the city.
- a person searching for residential real estate may perform a search not just on the typical search parameters of square footage, price range, number of rooms, and so forth, but may also search on analytically intensive parameters.
- search parameters of square footage, price range, number of rooms, and so forth
- analytically intensive parameters While there are unlimited ways that the principles may be applied, a select few diverse examples of how a flexible search for real estate might be accomplished will now be provided.
- search parameters the user might indicate a desire for any one or more of the following or other search items:
- an interactive view composition application involves two key times: authoring time, and use time.
- the functionality of the interactive view composition application is coded by a programmer to provide an interactive view composition that is specific to the desired domain.
- the author of an interior design application e.g., typically, a computer programmer
- a user e.g., perhaps a home owner or a professional interior designer
- the application might then use the application to perform any one or more of the set of finite actions that are hard coded into the application.
- the user might specify the dimensions of a virtual room being displayed, add furniture and other interior design components to the room, perhaps rotate the view to get various angles on the room, set the color of each item, and so forth.
- the user is limited to the finite set of actions that were enabled by the application author. For example, unless offered by the application, the user would not be able to use the application to automatically figure out which window placement would minimize ambient noise, how the room layout performs according to Feng Shui rules, or minimize solar heat contribution.
- the authoring component 240 is used to provide data to an existing pipeline 201 , where it is the data that drives the entire process from defining the input data, to defining the analytical model, to defining how the results of the analytics are visualized in the view composition. Accordingly, one need not perform any coding in order to adapt the pipeline 201 to any one of a wide variety of domains and problems. Only the data provided to the pipeline 201 is what is to change in order to apply the pipeline 201 to visualize a different view composition either from a different problem domain altogether, or to perhaps adjust the problem solving for an existing domain.
- the pipeline environment 200 may also include a taxonomy component 260 that organizes, categorizes, and relates data that is provided to the pipeline 201 .
- the taxonomy component 260 may be sensitive to the domain.
- an interior design domain, a road design domain, an architecture domain, and a Feng Shui domain may each have a different taxonomy that may be used to navigate through the data. This can be helpful since, as will be described below, there may be considerable data available to sift through due to composition activity in which the data set that is made available to the pipeline environment 200 may be ever increasing.
- the model can be modified and/or extended at runtime.
- the model can be modified and/or extended at runtime.
- the pipeline environment 200 also includes a user interaction response module 250 that detects when a user has interacted with the displayed view composition, and then determines what to do in response. For example, some types of interactions might require no change in the data provided to the pipeline 201 and thus require no change to the view composition. Other types of interactions may change one or more of the data 211 , 221 , or 231 . In that case, this new or modified data may cause new input data to be provided to the data portion 210 , might require a reanalysis of the input data by the analytics portion 220 , and/or might require a re-visualization of the view composition by the view portion 230 .
- the pipeline 201 may be used to extend data-driven analytical visualizations to perhaps an unlimited number of problem domains, or at least to a wide variety of problem domains. Furthermore, one need not be a programmer to alter the view composition to address a wide variety of problems.
- Each of the data portion 210 , the analytics portion 220 and the view portion 230 of the pipeline 201 will now be described with respect to the data portion 300 of FIG. 3 , the analytics portion 400 of FIG. 4 , and the view portion 500 of FIG. 5 , in that order.
- the taxonomy of the data is specific to the domain, thus allowing the organization of the data to be more intuitive to those operating in the domain. As will be apparent from FIGS.
- the pipeline 201 may be constructed as a series of transformation components where they each 1) receive some appropriate input data, 2) perform some action in response to that input data (such as performing a transformation on the input data), and 3) output data which then serves as input data to the next transformation component.
- the pipeline 201 may be implemented on the client, on the server, or may even be distributed amongst the client and the server without restriction.
- the pipeline 201 might be implemented on the server and provide rendering instructions as output.
- a browser at the client-side may then just render according to the rendering instructions received from the server.
- the pipeline 201 may be contained on the client with authoring and/or use performed at the client. Even if the pipeline 201 was entirely at the client, the pipeline 201 might still search data sources external to the client for appropriate information (e.g., models, connectors, canonicalizers, schemas, and others).
- the model is hosted on a server but web browser modules are dynamically loaded on the client so that some of the model's interaction and viewing logic is made to run on the client (thus allowing richer and faster interactions and views).
- FIG. 3 illustrates just one of many possible embodiments of a data portion 300 of the pipeline 201 of FIG. 2 .
- One of the functions of the data portion 300 is to provide data in a canonical format that is consistent with schemas understood by the analytics portion 400 of the pipeline discussed with respect to FIG. 4 .
- the data portion includes a data access component 310 that accesses the heterogenic data 301 .
- the input data 301 may be “heterogenic” in the sense that the data may (but need not) be presented to the data access component 310 in a canonical form.
- the data portion 300 is structured such that the heterogenic data could be of a wide variety of formats.
- Examples of different kinds of domain data that can be accessed and operated on by models include text and XML documents, tables, lists, hierarchies (trees), SQL database query results, BI (business intelligence) cube query results, graphical information such as 2D drawings and 3D visual models in various formats, and combinations thereof (i.e., a composite).
- the kind of data that can be accessed can be extended declaratively, by providing a definition (e.g., a schema) for the data to be accessed. Accordingly, the data portion 300 permits a wide variety of heterogenic input into the model, and also supports runtime, and declarative extension of accessible data types.
- the data access portion 300 includes a number of connectors for obtaining data from a number of different data sources. Since one of the primary functions of the connector is to place corresponding data into canonical form, such connectors will often be referred to hereinafter and in the drawings as “canonicalizers”. Each canonicalizer might have an understanding of the specific Application Program Interfaces (API's) of its corresponding data source. The canonicalizer might also include the corresponding logic for interfacing with that corresponding API to read and/or write data from and to the data source. Thus, canonicalizers bridge between external data sources and the memory image of the data.
- API's Application Program Interfaces
- the data access component 310 evaluates the input data 301 . If the input data is already canonical and thus processable by the analytics portion 400 , then the input data may be directly provided as canonical data 340 to be input to the analytics portion 400 .
- the data canonicalization components 330 are actually a collection of data canonicalization components 330 , each capable of converting input data having particular characteristics into canonical form.
- the collection of canonicalization components 330 is illustrated as including four canonicalization components 331 , 332 , 333 and 334 .
- the ellipsis 335 represents that there may be other numbers of canonicalization components as well, perhaps even fewer than the four illustrated.
- the input data 301 may even include a canonicalizer itself as well as an identification of correlated data characteristic(s).
- the data portion 300 may then register the correlated data characteristics, and provide the canonicalization component to the data canonicalization component collection 330 , where it may be added to the available canonicalization components. If input data is later received that has those correlated characteristics, the data portion 310 may then assign the input data to the correlated canonicalization component.
- Canonicalization components can also be found dynamically from external sources, such as from defined component libraries on the web. For example, if the schema for a given data source is known but the needed canonicalizer is not present, the canonicalizer can be located from an external component library, provided such a library can be found and contains the needed components.
- the pipeline might also parse data for which no schema is yet known and compare parse results versus schema information in known component libraries to attempt a dynamic determination of the type of the data, and thus to locate the needed canonicalizer components.
- the input data may instead provide a transformation definition defining canonicalization transformations.
- the collection 330 may then be configured to convert that transformations definition into a corresponding canonicalization component that enforces the transformations along with zero or more standard default canonicalization transformations. This represents an example of a case in which the data portion 300 consumes the input data and does not provide corresponding canonicalized data further down the pipeline. In perhaps most cases, however, the input data 301 results in corresponding canonicalized data 340 being generated.
- the data portion 310 may be configured to assign input data to the data canonicalization component on the basis of a file type and/or format type of the input data. Other characteristics might include, for example, the source of the input data.
- a default canonicalization component may be assigned to input data that does not have a designated corresponding canonicalization component. The default canonicalization component may apply a set of rules to attempt to canonicalize the input data. If the default canonicalization component is not able to canonicalize the data, the default canonicalization component might trigger the authoring component 240 of FIG. 2 to prompt the user to provide a schema definition for the input data.
- the authoring component 240 might present a schema definition assistant to help the author generate a corresponding schema definition that may be used to transform the input data into canonical form.
- the schema that accompanies the data provides sufficient description of the data that the rest of the pipeline 201 does not need new code to interpret the data. Instead, the pipeline 201 includes code that is able to interpret data in light of any schema that is expressible in accessible schema declaration language.
- canonical data 340 is provided as output data from the data portion 300 and as input data to the analytics portion 400 .
- the canonical data might include fields that include a variety of data types.
- the fields might include data types such as integers, floating point numbers, strings, vectors, arrays, collections, hierarchical structures, text, XML documents, tables, lists, SQL database query results, BI (business intelligence) cube query results, graphical information such as 2D drawings and 3D visual models in various formats, or even complex combinations of these various data types.
- the canonicalization process is able to canonicalize a wide variety of input data.
- the variety of input data that the data portion 300 is able to accept is expandable. This is helpful in the case where multiple models are combined as will be discussed later in this description.
- FIG. 4 illustrates analytics portion 400 which represents an example of the analytics portion 220 of the pipeline 201 of FIG. 2 .
- the data portion 300 provided the canonicalized data 401 to the data-model binding component 410 .
- the canonicalized data 401 might have any canonicalized form, and any number of parameters, where the form and number of parameters might even differ from one piece of input data to another.
- the canonical data 401 has fields 402 A through 402 H, which may collectively be referred to herein as “fields 402 ”.
- the analytics portion 400 includes a number of model parameters 411 .
- the type and number of model parameters may differ according to the model. However, for purposes of discussion of a particular example, the model parameters 411 will be discussed as including model parameters 411 A, 411 B, 411 C and 411 D.
- the identity of the model parameters, and the analytical relationships between the model parameters may be declaratively defined without using imperative coding.
- a data-model binding component 410 intercedes between the canonicalized data fields 402 and the model parameters 411 to thereby provide bindings between the fields.
- the data field 402 B is bound to model parameter 411 A as represented by arrow 403 A.
- the value from data field 402 B is used to populate the model parameter 411 A.
- the data field 402 E is bound to model parameter 411 B (as represented by arrow 403 B)
- data field 402 H is bound to model parameter 411 C (as represented by arrow 403 C).
- the data fields 402 A, 402 C, 402 D, 402 F and 402 G are not shown bound to any of the model parameters. This is to emphasize that not all of the data fields from input data are always required to be used as model parameters. In one embodiment, one or more of these data fields may be used to provide instructions to the data-model binding component 410 on which fields from the canonicalized data (for this canonicalized data or perhaps any future similar canonicalized data) are to be bound to which model parameter. This represents an example of the kind of analytics data 221 that may be provided to the analytics portion 220 of FIG. 2 .
- the definition of which data fields from the canonicalized data are bound to which model parameters may be formulated in a number of ways.
- the bindings may be 1) explicitly set by the author at authoring time, 2) explicitly set by the user at use time (subject to any restrictions imposed by the author), 3) automatic binding by the authoring component 240 based on algorithmic heuristics, and/or 4) prompting by the authoring component of the author and/or user to specify a binding when it is determined that a binding cannot be made algorithmically.
- bindings may also be resolved as part of the model logic itself.
- model parameters For instance, if one of the model parameters represents pressure, the author can name that model parameter “Pressure” or “P” or any other symbol that makes sense to the author.
- the author can even rename the model parameter which, in one embodiment, might cause the data model binding component 410 to automatically update to allow bindings that were previously bound to the model parameter of the old name to instead be bound to the model parameter of the new name, thereby preserving the desired bindings.
- This mechanism for binding also allows binding to be changed declaratively at runtime.
- the model parameter 411 D is illustrated with an asterisk to emphasize that in this example, the model parameter 411 D was not assigned a value by the data-model binding component 410 . Accordingly, the model parameter 411 D remains an unknown. In other words, the model parameter 411 D is not assigned a value.
- the modeling component 420 performs a number of functions. First, the modeling component 420 defines analytical relationships 421 between the model parameters 411 .
- the analytical relationships 421 are categorized into three general categories including equations 431 , rules 432 and constraints 433 . However, the list of solvers is extensible. In one embodiment, for example, one or more simulations may be incorporated as part of the analytical relationships provided a corresponding simulation engine is provided and registered as a solver.
- rules means a conditional statement where if one or more conditions are satisfied (the conditional or “if” portion of the conditional statement), then one or more actions are to be taken (the consequence or “then” portion of the conditional statement).
- a rule is applied to the model parameters if one or more model parameters are expressed in the conditional statement, or one or more model parameters are expressed in the consequence statement.
- a restriction means that a restriction is applied to one or more model parameters. For instance, in a city planning model, a particular house element may be restricted to placement on a map location that has a subset of the total possible zoning designations. A bridge element may be restricted to below a certain maximum length, or a certain number of lanes.
- the modeling component 420 may provide a mechanism for the author to provide a natural symbolic expression for equations, rules and constraints.
- an author of a thermodynamics related model may simply copy and paste equations from a thermodynamics textbook.
- the ability to bind model parameters to data fields allows the author to use whatever symbols the author is familiar with (such as the exact symbols used in the author's relied-upon textbooks) or the exact symbols that the author would like to use.
- the modeling component 420 Prior to solving, the modeling component 420 also identifies which of the model parameters are to be solved for (i.e., hereinafter, the “output model variable” if singular, or “output model variables” if plural, or “output model variable(s)” if there could be a single or plural output model variables).
- the output model variables may be unknown parameters, or they might be known model parameters, where the value of the known model parameter is subject to change in the solve operation.
- model parameters 411 A, 411 B and 411 C are known, and model parameter 411 D is unknown. Accordingly, unknown model parameter 411 D might be one of the output model variables.
- one or more of the known model parameters 411 A, 411 B and 411 C might also be output model variables.
- the solver 440 then solves for the output model variable(s), if possible.
- the solver 440 is able to solve for a variety of output model variables, even within a single model so long as sufficient input model variables are provided to allow the solve operation to be performed.
- Input model variables might be, for example, known model parameters whose values are not subject to change during the solve operation. For instance, in FIG. 4 , if the model parameters 411 A and 411 D were input model variables, the solver might instead solve for output model variables 411 B and 411 C instead.
- the solver might output any one of a number of different data types for a single model parameter. For instance, some equation operations (such as addition, subtraction, and the like) apply regardless of the whether the operands are integers, floating points, vectors of the same, or matrices of the same.
- the solver 400 might still present a partial solution for that output model variable, even if a full solve to the actual numerical result (or whatever the solved-for data type) is not possible.
- This allows the pipeline to facilitate incremental development by prompting the author as to what information is needed to arrive at a full solve. This also helps to eliminate the distinction between author time and use time, since at least a partial solve is available throughout the various authoring stages.
- the solver 440 is only able to solve for one of the output model variables “d”, and assign a value of 6 (an integer) to the model parameter called “d”, but the solver 440 is not able to solve for “c”. Since “a” depends from “c”, the model parameter called “a” also remains an unknown and unsolved for. In this case, instead of assigning an integer value to “a”, the solver might do a partial solve and output the string value of “c+11” to the model parameter “a”.
- the solver 440 is shown in simplified form in FIG. 4 . However, the solver 440 may direct the operation of multiple constituent solvers as will be described with respect to FIGS. 9 through 12 .
- the modeling component 420 then makes the model parameters 411 (including the now known and solved-for output model variables) available as output to be provided to the view portion 500 of FIG. 5 .
- FIG. 5 illustrates a view portion 500 which represents an example of the view portion 230 of FIG. 2 .
- the view portion 500 receives the model parameters 411 from the analytics portion 400 of FIG. 4 .
- the view portion also includes a view components repository 520 that contains a collection of view components.
- the view components repository 520 in this example is illustrated as including view components 521 through 524 , although the view components repository 520 may contain any number of view components.
- the view components each may include zero or more input parameters.
- view component 521 does not include any input parameters.
- view component 522 includes two input parameters 542 A and 542 B.
- View component 523 includes one input parameter 543
- view component 524 includes one input parameter 544 . That said, this is just an example.
- the input parameters may, but need not necessarily, affect how the visual item is rendered.
- the fact that the view component 521 does not include any input parameters emphasizes that there can be views that are generated without reference to any model parameters.
- Each view component 521 through 524 includes or is associated with corresponding logic that, when executed by the view composition component 540 using the corresponding view component input parameter(s), if any, causes a corresponding view item to be placed in virtual space 550 .
- That virtual item may be a static image or object, or may be a dynamic animated virtual item or object,
- each of view components 521 through 524 are associated with corresponding logic 531 through 534 that, when executed causes the corresponding virtual item 551 through 554 , respectively, to be rendered in virtual space 550 .
- the virtual items are illustrated as simple shapes. However, the virtual items may be quite complex in form perhaps even including animation. In this description, when a view item is rendered in virtual space, that means that the view composition component has authored sufficient instructions that, when provided to the rendering engine, the rendering engine is capable of displaying the view item on the display in the designated location and in the designated manner.
- the view components 521 through 524 may be provided perhaps even as view data to the view portion 500 using, for example, the authoring component 240 of FIG. 2 .
- the authoring component 240 might provide a selector that enables the author to select from several geometric forms, or perhaps to compose other geometric forms.
- the author might also specify the types of input parameters for each view component, whereas some of the input parameters may be default input parameters imposed by the view portion 500 .
- the logic that is associated with each view component 521 through 524 may be provided with view data, and/or may also include some default functionality provided by the view portion 500 itself
- the view portion 500 includes a model-view binding component 5 10 that is configured to bind at least some of the model parameters to corresponding input parameters of the view components 521 through 524 .
- model parameter 411 A is bound to the input parameter 542 A of view component 522 as represented by arrow 511 A.
- Model parameter 411 B is bound to the input parameter 542 B of view component 522 as represented by arrow 511 B.
- model parameter 411 D is bound to the input parameters 543 and 544 of view components 523 and 524 , respectively, as represented by arrow 511 C.
- the model parameter 411 C is not shown bound to any corresponding view component parameter, emphasizing that not all model parameters need be used by the view portion of the pipeline, even if those model parameters were essential in the analytics portion.
- model parameter 411 D is shown bound to two different input parameters of view components representing that the model parameters may be bound to multiple view component parameters.
- the definition of the bindings between the model parameters and the view component parameters may be formulated by 1) being explicitly set by the author at authoring time, 2) explicitly set by the user at use time (subject to any restrictions imposed by the author), 3) automatic binding by the authoring component 240 based on algorithmic heuristics, and/or 4) prompting by the authoring component of the author and/or user to specify a binding when it is determined that a binding cannot be made algorithmically.
- the view item may include an animation.
- an animation For example, consider for example a bar chart that plots a company's historical and projected revenues, advertising expenses, and profits by sales region at a given point in time (such as a given calendar quarter). A bar chart could be drawn for each calendar quarter in a desired time span. Now, imagine that you draw one of these charts, say the one for the earliest time in the time span, and then every half second replace it with the chart for the next time span (e.g., the next quarter). The result will be to see the bars representing profit, sales, and advertising expense for each region change in height as the animation proceeds.
- the chart for each time period is a “cell” in the animation, where the cell shows an instant between movements, where the collection of cells shown in sequence simulates movement.
- Conventional animation models allow for animation over time using built-in hard-coded chart types.
- any kind of visual can be animated, and the animation can be driven by varying any one or any combination of the parameters of the visual component.
- the animation can be driven by varying any one or any combination of the parameters of the visual component.
- the pipeline 201 is also distinguished in its ability to animate due to the following characteristics:
- the sequences of steps for the animation variable can be computed by the analytics of the model, versus being just a fixed sequence of steps over a predefined range.
- the advertising expense As the animation variable, imagine that what is specified is to “animate by advertising expense where advertising expense is increased by 5% for each step” or “where advertising expense is 10% of total expenses for that step”.
- a much more sophisticated example is “animate by advertising expense where advertising expense is optimized to maximize the rate of change of sales over time”.
- the solver will determine a set of steps for advertising spent over time (i.e., for each successive time period such as a quarter) such that the rate of growth of sales is maximized.
- the user presumably wants to see not only how fast sales can be made to grow by varying advertising expense, but also wants to learn the quarterly amounts for the advertising expenses that achieve this growth (the sequence of values could be plotted as part of the composite visual).
- any kind of visual can be animated, not just traditional data charts.
- CAD Computer-Aided Design
- Jet engines have limits on how fast turbines can be rotated before either the turbine blades lose integrity or the bearing overheats.
- this animation we desire that as air speed is varied the color of the turbine blades and bearing should be varied from blue (safe) to red (critical).
- the values for “safe” and “critical” turbine RPM and bearing temperature may well be calculated by the model based on physical characteristics of those parts.
- the pipeline 201 can be stopped mid stream so that data and parameters may be modified by the user, and the animation then restarted or resumed.
- the animation may be stopped at the point the runaway begins, modify some engine design criterion, such as the kind of bearing or bearing surface material, and then continue the animation to see the effect of the change.
- animations can be defined by the author, and/or left open for the user to manipulate to test various scenarios.
- the model may be authored to permit some visuals to be animated by the user according to parameters the user himself selects, and/or over data ranges for the animation variable that the user selects (including the ability to specify computed ranges should that be desired).
- Such animations can also be displayed side by side as in the other what-if comparison displays. For example, a user could compare an animation of sales and profits over time, animated by time, in two scenarios with differing prevailing interest rates in the future, or different advertising expense ramps. In the jet engine example, the user could compare the animations of the engine for both the before and after cases of changing the bearing design.
- FIG. 6 which illustrated 3-D renderings 600 of a view composition that includes a room layout 601 with furniture laid out within the room, and also includes a Feng Shui meter 602 .
- This example is provided merely to show how the principles described herein can apply to any arbitrary view composition, regardless of the domain. Accordingly, the example of FIG. 6 , and any other example view composition described herein, should be viewed strictly as only an example that allows the abstract concept to be more fully understood by reference to non-limiting concrete examples, and not defining the broader scope of the invention. The principles described herein may apply to construct a countless variety of view compositions. Nevertheless, reference to a concrete example can clarify the broader abstract principles.
- FIG. 7 illustrates a flowchart of a method 700 for generating a view construction.
- the method 700 may be performed by the pipeline environment 200 of FIG. 2 , and thus will be described with frequent reference to the pipeline environment 200 of FIG. 2 , as well as with reference to FIGS. 3 through 5 , which each show specific portions of the pipeline 201 of FIG. 2 . While the method 700 may be performed to construct any view composition, the method 700 will be described with respect to the view composition 600 of FIG. 6 . Some of the acts of the method 700 may be performed by the data portion 210 of FIG. 2 and are listed in the left column of FIG. 7 under the header “Data”. Other of the acts of the method 700 may be performed by the analytics portion 220 of FIG.
- the data portion accesses input data that at least collectively affects what visual items are displayed or how a given one or more of the visual items are displayed (act 711 ).
- the input data might include view components for each of the items of furniture. For instance, each of the couch, the chair, the plants, the table, the flowers, and even the room itself may be represented by a corresponding view component.
- the view component might have input parameters that are suitable for the view component. If animation were employed, for example, some of the input parameters might affect the flow of the animation. Some of the parameters might affect the display of the visual item, and some parameters might not.
- the room itself might be a view component.
- Some of the input parameters might include the dimensions of the room, the orientation of the room, the wall color, the wall texture, the floor color, the floor type, the floor texture, the position and power of the light sources in the room, and so forth.
- the room parameter might have a location of the room expressed in degrees, minutes, and seconds longitude and latitude.
- the room parameter might also include an identification of the author of the room component, and the average rental costs of the room.
- each plant may be configured with an input parameter specifying a pot style, a pot color, pot dimensions, plant color, plant resiliency, plant dependencies on sunlight, plant daily water intake, plant daily oxygen production, plant position and the like.
- a pot style a pot color
- pot dimensions a pot dimension
- plant color a pot dimension
- plant color a pot resiliency
- plant dependencies on sunlight a plant daily water intake
- plant daily oxygen production a plant position and the like.
- the Feng Shui meter 602 may also be a view component.
- the meter might include input parameters such as a diameter, a number of wedges to be contained in the diameter of the meter, a text color and the like.
- the various wedges of the Feng Shui meter may also be view components.
- the input parameters to the view components might be a title (e.g., water, mountain, thunder, wind, fire, earth, lake, heaven), perhaps a graphic to appear in the wedge, a color hue, or the like.
- the analytics portion binds the input data to the model parameters (act 721 ), determines the output model variables (act 722 ), and uses the model-specific analytical relationships between the model parameters to solve for the output model variables (act 723 ).
- act 721 has been previously discussed, and essentially allows flexibility in allowing the author to define the model analytics equations, rules and constraints using symbols that the model author is comfortable with.
- the more complex solver described with respect to FIGS. 9 through 12 may serve to solve for the output model variables (act 723 ).
- the identification of the output model variables may differ from one solving operation to the next. Even though the model parameters may stay the same, the identification of which model parameters are output model variables will depend on the availability of data to bind to particular model parameters. This has remarkable implications in terms of allowing a user to perform what-if scenarios in a given view composition.
- the Feng Shui room example of FIG. 6 suppose the user has bought a new chair to place in their living room. The user might provide the design of the room as data into the pipeline. This might be facilitated by the authoring component prompting the user to enter the room dimensions, and perhaps provide a selection tool that allows the user to select virtual furniture to drag and drop into the virtual room at appropriate locations that the actual furniture is placed in the actual room. The user might then select a piece of furniture that may be edited to have the characteristics of the new chair purchased by the user. The user might then drag and drop that chair into the room.
- the Feng Shui meter 602 would update automatically. In this case, the position and other attributes of the chair would be input model variables, and the Feng Shui scores would be output model variables.
- the Feng Shui scores of the Feng Shui meter would update, and the user could thus test the Feng Shui consequences of placing the virtual chair in various locations.
- the user can get local visual clues (such as, for example, gradient lines or arrows) that tell the user whether moving the chair in a particular direction from its current location makes things better or worse, and how much better or worse.
- the user could also do something else that is unheard of in conventional view composition.
- the user could actually change the output model variables. For instance, the user might indicate the desired Feng Shui score in the Feng Shui meter, and leave the position of the virtual chair as the output model variable.
- the solver would then solve for the output model variable and provide a suggested position or positions of the chair that would achieve at least the designated Feng Shui score.
- the user may choose to make multiple parameters output model variables, and the system may provide multiple solutions to the output model variables. This is facilitated by a complex solver that is described in further detail with respect to FIGS. 9 through 12 .
- the model parameters are bound to the input parameters of the parameterized view components (act 731 ). For instance, in the Feng Shui example, after the unknown Feng Shui scores are solved for, the scores are bound as input parameters to Feng Shui meter view component, or perhaps to the appropriate wedge contained in the meter. Alternatively, if the Feng Shui scores were input model variables, the position of the virtual chair may be solved for and provided as an input parameter to the chair view component.
- FSroom is an output model variable and its value, displayed on the meter, changes as the user repositions the chair.
- the view component can move the chair around, changing d, its distance from the wall, as the user changes the desired value, FSroom, on the meter.
- the view portion then constructs a view of the visual items (act 732 ) by executing the construction logic associated with the view component using the input parameter(s), if any, to perhaps drive the construction of the view item in the view composition.
- the view construction may then be provided to a rendering module, which then uses the view construction as rendering instructions (act 741 ).
- the process of constructing a view is treated as a data transformation that is performed by the solver. That is, for a given kind of view (e.g., consider a bar chart), there is a model consisting of rules, equations, and constraints that generates the view by transforming the input data into a displayable output data structure (called a scene graph) which encodes all the low level geometry and associated attributes needed by the rendering software to drive the graphics hardware.
- a scene graph a displayable output data structure
- the input data would be,. for example, the data series that is to be plotted, along with attributes for things like the chart title, axis labels, and so on.
- the model that generates the bar would have rules, equations, and constraints that would do things like 1) count how many entries the data series consists of in order to determine how many bars to draw, 2) calculate the range (min, max) that the data series spans in order to calculate things like the scale and starting/ending values for each axis, 3) calculate the height of the bar for each data point in the data series based on the previously calculated scale factor, 4) count how many characters are in the chart title in order to calculate a starting position and size for the title so that the title will be properly located and centered with respect to the chart, and so forth.
- the model is designed to calculate a set of geometric shapes based on the input data, with those geometric shapes arranged within a hierarchical data structure of type “scene graph”.
- the scene graph is an output variable that the model solves for based on the input data.
- an author can design entirely new kinds of views, customize existing views, and compose preexisting views into composites, using the same framework that the author uses to author, customize, and compose any kind of model.
- authors who are not programmers can create new views without drafting new code.
- FIG. 8 illustrates a flowchart of a method 800 for responding to user interaction with the view composition.
- the user interaction response module determines which components of the pipeline should perform further work in order to regenerate the view, and also provides data representing the user interaction, or that is at least dependent on the user interaction, to the pipeline components. In one embodiment, this is done via a transformation pipeline that runs in the reverse (upstream) view/analytics/data direction and is parallel to the (downstream) data/analytics/view pipeline.
- Each transformer in the data/analytics/view pipeline provides an upstream transformer that handles incoming interaction data. These transformers can either be null (passthroughs, which get optimized out of the path) or they can perform a transformation operation on the interaction data to be fed further upstream.
- This provides positive performance and responsiveness of the pipeline in that 1) interaction behaviors that would have no effect on upstream transformations, such as a view manipulation that has no effect on source data, can be handled at the most appropriate (least upstream) point in the pipeline and 2) intermediate transformers can optimize view update performance by sending heuristically-determined updates back downstream, ahead of the final updates that will eventually come from further upstream transformers. For example, upon receipt of a data edit interaction, a view-level transformer could make an immediate view update directly into the scene graph for the view (for edits it knows how to interpret), with the final complete update coming later from the upstream data transformer where the source data is actually edited.
- intermediate transformers can provide the needed upstream mapping. For example, dragging a point on a graph of a computed result could require a backwards solve that would calculate new values for multiple source data items that feed the computed value on the graph.
- the solver-level upstream transformer would be able to invoke the needed solve and to propagate upstream the needed data edits.
- FIG. 8 illustrates a flowchart of a method 800 for responding to user interaction with the view construction.
- it is first determined whether or not the user interaction requires regeneration of the view (decision block 802 ). This may be performed by the rendering engine raising an event that is interpreted by the user interaction response component 250 of FIG. 2 . If the user interaction does not require regeneration of the view (No in decision block 802 ), then the pipeline does not perform any further action to reconstruct the view (act 803 ), although the rendering engine itself may perform some transformation on the view.
- An example of such a user interaction might be if the user were to increase the contrast of the rendering of the view construction, or rotate the view construction. Since those actions might be undertaken by the rendering engine itself, the pipeline need perform no work to reconstruct the view in response to the user interaction.
- the view is reconstructed by the pipeline (act 804 ). This may involve some altering of the data provided to the pipeline. For instance, in the Feng Shui example, suppose the user were to move the position of the virtual chair within the virtual room, the position parameter of the virtual chair component would thus change. An event would be fired informing the analytics portion that the corresponding model parameter representing the position of the virtual chair should be altered as well. The analytics component would then resolve the Feng Shui scores, repopulate the corresponding input parameters of the Feng Shui meter or wedges, causing the Feng Shui meter to update with current Feng Shui scores suitable for the new position of the chair.
- the user interaction might require that model parameters that were previously known are now unknown, and that previously unknown parameters are now known. That is one of several possible examples that might require a change in designation of input and output model variables such that previously designated input model variables might become output model variables, and vice versa. In that case, the analytics portion would solve for the new output model variable(s) thereby driving the reconstruction of the view composition.
- FIG. 9 illustrates a solver environment 900 that may represent an example of the solver 440 of FIG. 4 .
- the solver environment 900 may be implemented in software, hardware, or a combination.
- the solver environment 900 includes a solver framework 901 that manages and coordinates the operations of a collection 910 of specialized solvers.
- the collection 910 is illustrated as including three specialized solvers 911 , 912 and 913 , but the ellipsis 914 represents that there could be other numbers (i.e., more than three or less than three) of specialized solvers as well.
- the ellipsis 914 also represents that the collection 910 of specialized solvers is extensible.
- FIG. 9 illustrates that a new solver 915 is being registered into the collection 910 using the solver registration module 921 .
- a new solver might be perhaps a simulation solver which accepts one or more known values, and solves for one or more unknown values.
- Other examples include solvers for systems of linear equations, differential equations, polynomials, integrals, root-finders, factorizers, optimizers, and so forth. Every solver can work in numerical mode or in symbolic mode or in mixed numeric-symbolic mode.
- the numeric portions of solutions can drive the parameterized rendering downstream.
- the symbolic portions of the solution can drive partial solution rendering.
- the collection of specialized solvers may include any solver that is suitable for solving for the output model variables. If, for example, the model is to determine drag of a bicycle, the solving of complex calculus equations might be warranted. In that case, a specialized complex calculus solver may be incorporated into the collection 910 to perhaps supplement or replace an existing equations solver.
- each solver is designed to solve for one or more output model variables in a particular kind of analytics relationship. For example, there might be one or more equation solvers configured to solve for unknowns in an equation. There might be one or more rules solvers configured to apply rules to solve for unknowns. There might be one or more constraints solvers configured to apply constraints to thereby solve for unknowns. Other types of solves might be, for example, a simulation solver which performs simulations using input data to thereby construct corresponding output data.
- the solver framework 901 is configured to coordinate processing of one or more or all of the specialized solvers in the collection 910 to thereby cause one or more output model variables to be solved for.
- the solver framework 901 is then configured to provide the solved for values to one or more other external components.
- the solver framework 901 may provide the model parameter values to the view portion 230 of the pipeline, so that the solving operation thereby affects how the view components execute to render a view item, or thereby affect other data that is associated with the view item.
- the model analytics themselves might be altered.
- the model might be authored with modifiable rules set so that, during a given solve, some rule(s) and/or constraint(s) that are initially inactive become activated, and some that are initially activated become inactivated. Equations can be modified this way as well.
- FIG. 10 illustrates a flowchart of a method 1000 for the solver framework 901 to coordinate processing amongst the specialized solvers in the collection 910 .
- the method 1000 of FIG. 10 will now be described with frequent reference to the solver environment 900 of FIG. 9 .
- the solver framework begins a solve operation by identifying which of the model parameters are input model variables (act 1001 ), and which of the model parameters are output model variables (act 1002 ), and by identifying the model analytics that define the relationship between the model parameters (act 1003 ). Given this information, the solver framework analyzes dependencies in the model parameters (act 1004 ). Even given a fixed set of model parameters, and given a fixed set of model analytics, the dependencies may change depending on which of the model parameters are input model variables and which are output model variables. Accordingly, the system can infer a dependency graph each time a solve operation is performed using the identity of which model parameters are input, and based on the model analytics. The user need not specify the dependency graph for each solve.
- the solver framework By evaluating dependencies for every solve operation, the solver framework has the flexibility to solve for one set of one or more model variables during one solve operation, and solve for another set of one or more model variables for the next solve operation.
- the model may not have any output model variables at all.
- the solve will verify that all of the known model parameter values, taken together, satisfy all the relationships expressed by the analytics for that model. In other words, if you were to erase any one data value, turning it into an unknown, and then solve, the value that was erased would be recomputed by the model and would be the same as it was before.
- a model that is loaded can already exist in solved form, and of course a model that has unknowns and gets solves now also exists in solved form.
- an order of execution of the specialized solvers is determined based on the analyzed dependencies (act 1007 ).
- the solvers are then executed in the determined order (act 1008 ).
- the order of execution may be as follows 1) equations with dependencies or that are not fully solvable as an independent expression are rewritten as constraints 2) the constraints are solved, 3) the equations are solved, and 4) the rules are solved.
- the rules solving may cause the data to be updated.
- the solvers are executed in the designated order, it is then determined whether or not solving should stop (decision block 1009 ).
- the solving process should stop if, for example, all of the output model variables are solved for, or if it is determined that even though not all of the output model variables are solved for, the specialized solvers can do nothing further to solve for any more of the output model variables. If the solving process should not end (No in decision block 1009 ), the process returns back to the analyzing of dependencies (act 1004 ). This time, however, the identity of the input and output model variables may have changed due to one or more output model variables being solved for. On the other hand, if the solving process should end (Yes in decision block 1009 ) the solve ends (act 1010 ).
- This method 1000 may repeat each time the solver framework detects that there has been a change in the value of any of the known model parameters, and/or each time the solver framework determines that the identity of the known and unknown model parameters has changed.
- Solving can proceed in at least two ways. First, if a model can be fully solved symbolically (that is, if all equations, rules, and constraints can be algorithmically rewritten so that a computable expression exists for each unknown) then that is done, and then the model is computed. In other words, data values are generated for each unknown, and/or data values that are permitted to be adjusted are adjusted.
- a model cannot be fully solved symbolically, it is partially solved symbolically, and then it is determined if one or more numerical methods can be used to effect the needed solution. Further, an optimization step occurs such that even in the first case, it is determined whether use of numerical methods may be the faster way to compute the needed values versus performing the symbolic solve method.
- the symbolic method can be faster, there are cases where a symbolic solve may perform so many term rewrites and/or so many rewriting rules searches that it would be faster to abandon this and solve using numeric methods.
- FIG. 11 illustrates a solver environment 1100 that represents an example of the solver environment 900 of FIG. 9 .
- the solver coordination module 1110 acts to receive the input model variables 1011 and coordinate the actions of the forward solver 1121 , the symbolic solver 1122 (or the “Inverter”), and the numeric solver 1123 such that the model variables 1102 (including the output model variables) are generated.
- the forward solver 1121 , the symbolic solver 1122 and the numeric solver 1123 are examples of the solvers that might be in the solver collection 910 of FIG. 9 .
- the solver coordination module 1110 maintains a dependency graph of the model analytics that have corresponding model variables. For each solve operation, the solver coordination module 1110 may determine which of the model variables are input model variables, and which of the model variables are output model variables and thus are to be solved for.
- numeric solver 1123 is provided to solve model analytics using model analytics in the case where the model analytics are not properly invertible (either because inversion was not possible, not known, or not enabled by the symbolic solver).
- the solver coordination module 1110 is configured to manage each solve operation.
- FIG. 12 illustrates a flowchart of a method 1200 for managing the solve operation such that model analytics may be solved for.
- the method 1200 may be managed by the solver environment 100 under the direction of the solver coordination module 1110 .
- the solver coordination module 1110 identifies which of the model variables of the model analytics are input variable(s) for a particular solve, and which of the model variables are output model variable(s) for a particular solve (act 1201 ). If, for example, the input and output model variables are defined in FIG. 4 by the data-model binder component 410 , even given a constant set of model variables, the identity of the input model variables and the output model variables may change from one solve operation to the next. Accordingly, the coordination of the solve operation may change from one solve operation to the next.
- a forward solve may be sufficient for one solve operation
- an inversion and a forward solve of the inverted analytics may be sufficient for another solve operation
- a numeric solve may be sufficient for yet another solve operation.
- model analytics may change as the model analytics are formulated or perhaps combined with other model analytics as previously described.
- the solver environment 100 may account for these changes by identifying the input and output model variables whenever there is a change, by accounting for any changed model analytics, and solving appropriately.
- the solver coordination module 1110 determines whether or not a forward solve of the output parameter(s) is to be performed given the input model variables (s) without first inverting the model analytics (decision block 1202 ). If a forward solve is to be performed (Yes in decision block 1202 ), the forward solver 1121 is made to forward solve the model analytics (act 1203 ). This forward solve may be of the entire model analytics, or of only a portion of the model analytics. In the latter case, the method 1200 may be executed once again, only this time with a more complete set of input model variables that include the model variables solved for in the forward solve.
- the model analytics is inverted for the particular solve such that a forward solve may solve for the output parameter(s) (decision block 1204 ). If the model analytics (or at least a portion of the model analytics) is to be inverted (Yes in decision block 1204 ), the model analytics is inverted by the symbolic solver (act 1205 ). Thereafter, the inverted model analytics may be solved for using a forward solve (act 1203 ). Once again, if only a portion of the model analytics was solved for in this way, the method 1200 may be executed again, but with an expanded set of input model variables.
- the numeric solver may solve for the output variable(s) using numeric methods (act 1206 ). Once again, if only a portion of the model analytics was solved for in this way, the method 1200 may be executed again, but with an expanded set of input model variables.
- the pipeline environment 200 also includes a model importation mechanism 241 that is perhaps included as part of the authoring mechanism 240 .
- the model importation mechanism 241 provides a user interface or other assistance to the author to allow the author to import at least a portion of a pre-existing analytics-driven model into the current analytics-driven model that the user is constructing. Accordingly, the author need not always begin from scratch when authoring a new analytics model.
- the importation may be of an entire analytics-driven model, or perhaps a portion of the model. For instance, the importation may cause one or more or all of the following six potential effects.
- additional model input data may be added to the pipeline.
- additional data might be added to the input data 211 , the analytics data 221 and/or the view data 231 .
- the additional model input data might also include additional connectors being added to the data access component 310 of FIG. 3 , or perhaps different canonicalization components 330 .
- the data-model binder 410 may cause additional bindings to occur between the canonicalized data 401 and the model parameters 411 . This may cause an increase in the number of known model parameters.
- model parameters 411 may be augmented due to the importation of the analytical behaviors of the imported model.
- any one of more of these additional items may be viewed as additional data that affects the view composition. Furthermore, any one or more of these effects could change the behavior of the solver 440 of FIG. 4 .
- the model-view binding component 510 binds a potentially augmented set of model parameters 411 to a potentially augmented set of view components in the view component repository 520 .
- the data associated with that model is imported. Since the view composition is data-driven, this means that the imported portions of the model are incorporated immediately into the current view composition.
- the Feng Shui room view composition of FIG. 6 As an example of how useful this feature might be, consider the Feng Shui room view composition of FIG. 6 .
- the author of this application may be a Feng Shui expert, and might want to just start from a standard room layout view composition model. Accordingly, by importing a pre-existing room layout model, the Feng Shui expert is now relatively quickly, if not instantly, able to see the room layout 601 show up on the display shown in FIG. 6 . Not only that, but now the furniture and room item catalog that normally might come with the standard room layout view composition model, has now become available to the Feng Shui application of FIG. 6 .
- the Feng Shui expert might want to import a basic pie chart element as a foundation for building the Feng Shui chart element 602 .
- the Feng Shui expert might specify specific fixed input parameters for the chart element including perhaps that there are 8 wedges total, and perhaps a background image and a title for each wedge.
- the Feng Shui expert need only specify the analytical relationships specifying how the model parameters are interrelated. Specifically, the color, position, and type of furniture or other room item might have an effect on a particular Feng Shui score. The expert can simply write down those relationships, to thereby analytically interconnect the room layout 601 and the Feng Shui score. This type of collaborative ability to build on the work of others may generate a tremendous wave of creativity in creating applications that solve problems and permit visual analysis.
- FIG. 6 illustrates a single view composition generated from a set of input data
- the principles described herein can be extended to an example in which there is an integrated view composition that includes multiple constituent view compositions. This might be helpful in a number of different circumstances.
- constituent view compositions might each represent one of multiple possible solutions, where another constituent view composition might represent another possible solution.
- a user simply might want to retain a previous view composition that was generated using a particular set of input data, and then modify the input data to try a new scenario to thereby generate a new view composition.
- the user might then want to retain also that second view composition, and try a third possible scenario by altering the input data once again.
- the user could then view the three scenarios at the same time, perhaps through a side-by-side comparison, to obtain information that might otherwise be difficult to obtain by just looking at one view composition at a time.
- FIG. 13 illustrates an integrated view composition 1300 that extends from the Feng Shui example of FIG. 6 .
- the first view composition 600 of FIG. 6 is represented once again using elements 601 and 602 , exactly as shown in FIG. 6 .
- the second view composition is similar to the first view composition in the there are two elements, a room display and a Feng Shui score meter.
- the input data for the second view composition was different than the input data for the first view composition.
- the position data for several of the items of furniture would be different thereby causing their position in the room layout 1301 of the second view composition to be different than that of the room layout 601 of the first view composition.
- the different position of the various furniture items correlates to different Feng Shui scores in the Feng Shui meter 1302 of the second view composition as compared to the Feng Shui meter 602 of the first view composition.
- the integrated view composition may also include a comparison element that visually represents a comparison of a value of at least one parameter across some of all of the previously created and presently displayed view compositions. For instance, in FIG. 13 , there might be a bar graph showing perhaps the cost and delivery time for each of the displayed view compositions. Such a comparison element might be an additional view component in the view component repositor 520 . Perhaps that comparison view element might only be rendered if there are multiple view compositions being displayed. In that case, the comparison view composition input parameters may be mapped to the model parameters for different solving iterations of the model. For instance, the comparison view composition input parameters might be mapped to the cost parameter that was generated for both of the generations of the first and second view compositions of FIG. 13 , and mapped to the delivery parameter that was generated for both of the generations of the first and second view compositions.
- the selection mechanism 1310 is illustrated as including three possible view constructions 1311 , 1312 and 1313 , that are illustrated in thumbnail form, or are illustrated in some other deemphasized manner.
- Each thumbnail view composition 1311 through 1313 includes a corresponding checkbox 1321 through 1323 .
- the user might check the checkbox corresponding to any view composition that is to be visually emphasized. In this case, the checkboxes 1321 and 1323 are checked, thereby causing larger forms of the corresponding view constructions to be displayed.
- the integrated view composition may have a mechanism for a user to interact with the view composition to designate what model parameters should be treated as an unknown thereby triggering another solve by the analytical solver mechanism. For instance, in the room display 1301 of FIG. 13 , one might right click on a particular item of furniture, right click on a particular parameter (e.g., position), and a drop down menu might appear allowing the user to designate that the parameter should be treated as unknown. The user might then right click on the harmony percentage (e.g., 95% in the Feng Shui score meter 1302 ), whereupon a slider might appear (or a text box of other user input mechanism) that allows the user to designate a different harmony percentage. Since this would result in the identity of the known and unknown parameters being changed, a re-solve would result, and the item of furniture whose position was designated as an unknown might appear in a new location.
- the harmony percentage e.g. 95% in the Feng Shui score meter 1302
- the integrated view composition might also include a visual prompt for an adjustment that could be made that might trend a value of a model parameter in a particular direction.
- a visual prompt for an adjustment that could be made that might trend a value of a model parameter in a particular direction.
- various positions of the furniture might be suggested for that item of furniture whose position was designated as an unknown.
- the view component might also show shadows where the chair could be moved to increase a particular score.
- a user might use those visual prompts in order to improve the design around a particular parameter desired to be optimized.
- FIGS. 1 and 2 may allow countless data-driven analytics models to be constructed, regardless of the domain. There is nothing at all that need be similar about these domains. Wherever there is a problem to be solved where it might be helpful to apply analytics to visuals, the principles described herein may be beneficial. Up until now, only a few example applications have been described including a Feng Shui room layout application. To demonstrate the wide-ranging applicability of the principles described herein, several additional wide-ranging example applications will now be described.
- FIG. 14 illustrates an example retailer shelf arrangement visualization.
- the input data might include visual images of the product, a number of the product, a linear square footage allocated for each product, and shelf number for each product, and so forth.
- FIG. 15 illustrates an example visualized urban plan.
- FIG. 16 is an illustration about children's education.
- FIG. 17 is a conventional illustration about population density.
- visualizations are just static illustrations. With the principles described herein, these can become live, interactive experiences. For instance, by inputting a geographically distributed growth pattern as input data, a user might see the population peaks change. Some visualizations, where the authored model supports this, will let users do what-ifs. That is, the author may change some values and see the effect on that change on other values.
- the principles described herein provide a major paradigm shift in the world of visualized problem solving and analysis.
- the paradigm shift applies across all domains as the principles described herein may apply to any domain.
- the pipeline 201 is data-driven.
- input data 211 is provided to data portion 210
- analytics data 221 is provided to analytics portion 220
- view data 231 is provided to view portion 230 .
- Examples of each of these data have already been described. Suffice it to say that the volume of data that could be selected by the authoring component 240 may be quite large, especially given the ease of composition in which portions of models can be imported into a model to compose more and more complex models.
- a taxonomy component 260 provides a number of domain-specific taxonomies of the input data.
- FIG. 18 illustrates a taxonomy environment 1800 in which the taxonomy component 260 may operate.
- Taxonomy involves the classification of items into categories and the relating of those categories.
- the environment 1800 thus includes a collection of items 1810 that are to be subjected to taxonomization.
- the collection of items 1810 is illustrated as including only a few items altogether including items 1811 A through 1811 P (referred to collectively as “member items 1811 ”). Although only a few member items 1811 are shown, there may be any number of items, perhaps even hundreds, thousands or even millions of items that should be categorized and taxonomized as represented by the ellipsis 1811 Q.
- the member items 1811 include the pool of member items from which the authoring component 240 may select in order to provide the data 211 , 221 and 231 to the pipeline 201 .
- a domain-sensitive taxonomization component 1820 accesses all or a portion of the member items 1811 , and also is capable of generating a distinct taxonomy of the member items 1811 . For instance, the taxonomization component 1820 generates domain specific taxonomies 1821 . In this case, there are five domain-specific taxonomies 1821 A through 1821 E, amongst potentially others as represented by the ellipsis 1821 F. There may also be fewer than five domain-specific taxonomies created and managed by the taxonomization component 1820 .
- taxonomy 1821 A might taxonomize the member items suitable for a Feng Shui domain
- taxonomy 1821 B may taxonomize the member items suitable for a motorcycle design domain
- taxonomy 1821 C may likewise be suitable for a city planning domain
- taxonomy 1821 D may be suitable for an inventory management domain
- taxonomy 1821 E may be suitable for an abstract artwork domain.
- these are just five of the potentially countless number of domains that may be served by the pipeline 201 .
- Each of the taxonomies may use all or a subset of the available member items to classify in the corresponding taxonomy.
- FIG. 19 illustrates one specific and simple example 1900 of a taxonomy of the member items.
- the taxonomy may be the domain-specific taxonomy 1821 A of FIG. 18 .
- the taxonomy 1900 includes category node 1910 that includes all of the member items 1811 except member items 1811 A and 1811 E.
- the category node 1910 may be an object that, for example, includes pointers to the constituent member items and thus in the logical sense, the member items may be considered “included within” the category node 1910 .
- the category node 1910 also has associated therewith a properties correlation descriptor 1911 that describes the membership qualifications for the category node 1910 using the properties of the candidate member items. When determining whether or not a member item should be included in a category, the properties correlation descriptor may be used to evaluate the descriptor against the properties of the member item.
- two categories can be related to each other in a number of different ways.
- One common relation is that one category is a subset of another. For example, if there is a “vehicle” category that contains all objects that represent vehicles, there might be a “car” category that contains a subset of the vehicles category.
- the property correlation descriptors of both categories may define the specific relationship. For instance, the property correlation descriptor for the vehicles category may indicate that objects having the following properties will be included in the category: 1) the object is movable, 2) the object may contain a human.
- the car category property correlation descriptor may include these two property requirements either expressly or implicitly, and may also include the following property requirements: 1) the object contains at least 3 wheels that maintain contact with the earth during motion of the object, 2) the object is automotive, 3) the height of the object does not exceed 6 feet.
- the taxonomization component may assign an object to one or more categories in any given domain-specific taxonomy, and may also understand the relationship between the categories.
- a second category node 1920 that includes another property correlation descriptor 1921 .
- the category node 1920 logically includes all member items that satisfy the property correlation descriptor 1921 .
- the member items logically included in the category node are a subset of the member items included in the first category node 1910 (e.g. including member items 1811 F, 1811 J, 1811 N and 1811 P). This could be because the property correlation descriptor 1921 of the second category node 1920 specifies the same property requirements as the correlation descriptor 1911 of the first category node 1910 , except for one or more additional property requirements.
- the relation between the first category node 1910 and the second category node 1920 is logically represented by relation 1915 .
- the relationship between the categories is a subset relation. That is, one category (e.g., the car category) is a subset of the other (e.g., the vehicle category).
- one category e.g., the car category
- the other category e.g., the vehicle category
- there might be a majority inheritance relationship in which if a majority (or some specified percentage) of the objects in one category have a particular property value, the objects in another category have this property and inherit this property value.
- There might be a “similar color” relationship in which if one category of objects has a primary color within a certain wavelength range of visible light, then the other category contains objects having a primary color within a certain neighboring wavelength range of visible light.
- taxonomies will now be described in an abstract sense. Examples of abstractly represented taxonomies are shown in FIGS. 20A through 20C . Then specific examples will be described, understanding that the principles described herein enable countless applications of domain-specific taxonomies in a data-driven visualization.
- FIG. 19 is a simple two category node taxonomy
- the examples of FIGS. 20A through 20C are more complex.
- Each node in the taxonomies 2000 A through 2000 C of FIGS. 20A through 20C represents a category node that contains zero or more member items, and may have a property correlation descriptor associated with each that is essentially an admission policy for admitting member items into the category node.
- the member items and property correlation descriptor for each of the category nodes of the taxonomies 2000 A through 2000 C are not illustrated.
- the lines between the category nodes represent the relations between category nodes. They might be a subset relation or some other kind of relation without limit.
- the precise nature of the relations between category nodes is not critical. Nevertheless, to emphasize that there may be a variety of relation types between category nodes in the taxonomy, the relations are labeled with an A, B, C, D, or E.
- FIGS. 20A through 20C are provided just as an example.
- the precise structure of the taxonomies of FIGS. 20A through 20C is not only not critical, but the principles described herein permit great flexibility in what kinds of taxonomies can be generated even based on the same set of input candidate member items.
- the taxonomy 2000 A includes category node 2001 A through 2010 A related to each other using relation types A, B and C.
- Taxonomy 2000 B includes categories 2001 B through 2008 B related to each other using relation types B, C and D.
- Taxonomy 2000 C includes categories 2001 C through 2012 C related to each other using relation types C, D and E.
- taxonomies 2000 A and 2000 B are hierarchical, whereas taxonomy 2000 C is more of a non-hierarchical network.
- those candidate member items may be evaluated against the property correlation descriptor of each of the category nodes in each of the taxonomies. If the properties of the member item have values that permit the member item to satisfy the requirements of the property correlation descriptor (i.e., the admission policy), the member item is admitted into the category node. For instance, perhaps a pointer to the member item is added to the category node. Thus, if new member items have sufficient numbers of properties, new member items may be imported automatically into appropriate categories in all of the taxonomies.
- FIG. 21 illustrates a member item 2100 that includes multiple properties 2101 .
- the member item 2100 is illustrated as including five properties 2101 A, 2101 B, 2101 C, 2101 D, and 2101 E amongst potentially others as represented by the ellipsis 2101 F.
- properties 2101 A, 2101 B, 2101 C, 2101 D, and 2101 E amongst potentially others as represented by the ellipsis 2101 F.
- potential data for each of the data portion 210 , the analytics portion 220 , and the view portion 230 may be taxonomized. For example, consider the domain in which the author is composing a consumer application that allows an individual (such as a consumer or neighborhood resident) to interface with a map of a city.
- a taxonomy for view data 231 that can be selected.
- a building category that includes all of the buildings.
- buildings There might be different types of buildings: government buildings, hospitals, restaurants, houses, and so forth.
- a transit category that includes railroad, roadways, and canals sub-categories.
- the roadways category might contain categories or objects representing streets, highways, bike-paths, overpasses, and so forth.
- the streets category might include objects or categories of visual representations of one way streets, multi-line streets, turn lanes, center lanes, and so forth.
- Parking category There might be a parking category showing different types of visual representations of parking or other sub-categories of parking (e.g., multi-level parking, underground parking, street parking, parking lots, and so forth). Parking might also be sub-categorized by whether or not parking is free, or whether there is a cost.
- sub-categories of parking e.g., multi-level parking, underground parking, street parking, parking lots, and so forth. Parking might also be sub-categorized by whether or not parking is free, or whether there is a cost.
- a parking structure might have data associated with it such as, for example, 1) whether the parking is valet parking, 2) what the hourly change is for the parking, 3) the hours that the parking is open, 4) whether the parking is patrolled by security, and if so, how many security officers there are per unit area of parking, 5) the number of levels of the parking, 6) the square footage of the parking if there is but one level, and if multi-level parking, the square footage on each level, 7) the annualized historical number of car thefts that occur in the parking structure, 8) the volume usage of the parking, 9) whether parking is restricted to the satisfaction of one or more conditions (i.e., employment at a nearby business, patronage at a restaurant or mall, and so forth), or any other data that might be helpful.
- the analytics might present cost-based analytics in one category, time-based analytics in another category, distance-based analytics in yet another category, directory analytics in another category, and routing analytics in another category.
- the analytics are taxonomized to assist the author in formulating an analytical model for the desired application.
- the routing analytics category might include a category for equations that calculate a route, a constraint that specifies what restrictions can be made on the routing (such as shortest route, most use of highways, avoid streets, and so forth), or rules (such as traffic directions on particular roads). Similar subcategories might also be included for the other categories as well.
- the domain is city planning.
- the problems to be solved are different than those to be solved in the consumer domain. Accordingly, the taxonomy of the analytics may be laid out much differently for the city planning domain as compared to the consumer domain, even though both deal with a city topology.
- a tractor design domain might be interested in a whole different set of analytics, and would use a different taxonomy.
- the visual items of a tractor design domain might be totally different than that of a city planning domain. No longer is there a concern for city visual elements.
- the various visual elements that make up a tractor are taxonomized.
- the visual items might be taxonomized using a relation of what can be connected to what. For instance, there might be a category for “Things that can be connected to the seat”, “Things that can be connected to the carburetor, “Things that can be connected to the rear axle” and so forth.
- There might also be a different analytics taxonomy For instance, there might be a constraint regarding tread depth on the tires considering that the tractor needs to navigate through wet soil.
- analytics that calculate the overall weight of the tractor or its subcomponents, and so forth.
- FIG. 22 illustrates a domain-specific taxonomy 2200 and represents one example of the domain-specific taxonomies 1821 of FIG. 18 .
- the domain-specific taxonomy includes a data taxonomy 2201 in which at least some of the available data items are taxonomized into corresponding related categories, a view component taxonomy 2203 in which at least some of the available view components are taxonomized into corresponding related view components categories, and an analytics taxonomy 2202 in which at least some of the available analytics are taxonomized into correlating related analytics categories. Examples of such domain specific taxonomies in which data, analytics, and view components are taxonomized in a manner that is specific to domain have already been described.
- FIG. 23 illustrates a method for navigating and using analytics.
- the analytics component 220 is accessed (act 2301 ) along with the corresponding domain specific analytics taxonomy (act 2303 ). If there are multiple domain-specific analytics taxonomies, the domain may first be identified (act 2302 ), before the domain-specific analytics taxonomy may be accessed (act 2303 ).
- the analytics taxonomy may be navigated (act 2304 ) by traversing the related categories.
- This navigation may be performed by a human being with the assistance of a computing system, or may be performed even by a computing system alone without the contemporaneous assistance of a human being.
- a computer or human may derive information from the correlation property descriptor for each category that defines that admission policy for analytics to be entered into that category. Information may also be derived by the relationships between categories.
- the navigation may be used to solve the analytics problem thereby solving for output model parameters, or perhaps for purposes of merging analytics from multiple models. Alternatively, the navigation may be used to compose the anaytics model in the first place.
- FIG. 24 illustrates a flowchart of a method 2400 for searching using the data-driven analytics model.
- the method 2400 may be performed each time the search tool 242 receives or otherwise accesses a search request (act 2401 ).
- search request entry mechanisms perhaps the search request is text-based and entered directly into a text search field.
- radio buttons are filled in to enter search parameters.
- a slider might also be used to enter a range for the search parameter.
- the search request generation may have been generated in an interactive fashion with the user. For example, in the case where the user requests real estate that experiences a certain noise level range, the application might generate noise that gets increasingly louder, and ask the user to hit the “Too Much Noise” button when the noise gets louder than the user wants to bear.
- the search request is not a conventional search request, but may require solving operations of the data-driven analytics model.
- the search tool 242 of FIG. 2 identifies any model parameters that should be solved for in order to be able to respond to the request (act 2402 ). This might be accomplished using, for example, the various taxonomies discussed above. For instance, in the case where the user is searching for real estate that is not in the shadow of a mountain after 9:15 at any point of the year, there might be a model variable called “mountain shade” that is solved for. In the case where the user searches for real estate that experiences certain noise levels, there might be a model variable called “average noise” that is to be solved for given a particular coordinate.
- the analytical relations of the analytics portion 220 are used to solve for the output variables (act 2403 ).
- the solved output variable(s) are then used by the search tool 242 to formulate a response to the search request using the solved value(s) (act 2404 ).
- the user might interact with the method 2400 as the method 2400 is being executed, the method 2400 may be performed by a computing system without contemporaneous assistance from a human being.
- the search request may be issued by a user or perhaps even by another computing or software module.
- the method 2400 may be repeated numerous times each time a search request is processed.
- the model variables that are solved for may, but need not, be different for each search request. For example, there may be three search requests for homes that have a certain price range, and noise levels. For example, there might be a search request for homes in the $400,000 to $600,000 price range, and whose average noise levels are below 50 decibels. The parameters to be solved for here would be the noise levels.
- a second search request may be for homes in the $200,000 to $500,000 price range, and whose noise levels are below 60 decibels. Here the parameters to be solved for would once again be noise levels. Note, however, that in the second search request, some of the solving operations would have already been done for the search request.
- the system already identified homes in the $400,000 to $500,000 price range whose noise levels were below 50 decibels. Thus, for those houses, there is no need to recalculate the noise levels. Once solved, those values may be kept for future searching. Thus, this allows a user to perform exploration by submitting follow-on requests. The user might then submit a third search request for houses in the $400,000 to $500,000 price range, and having noise levels less than 45 decibels. Since noise levels for those homes have already been solved for, there is no need to solve for them again. Accordingly, the search results can be returned with much less computation. In essence, the system may learn new information by solving problems, and be able to take advantage of that new information to solve other problems.
- each search request might involve solving for different output model variables. For instance, after performing the search requests just described, the user might submit a search request for houses that are not in the shadow of a mountain. Once the system solves for this, whenever this or another user submits a similar request, the results from the solve may be used to fulfill that subsequent search request.
- a user might submit a search request for houses that would stay standing in a magnitude 8.0 earthquake, causing a simulation to verify that each house would either stay standing, fall, or perhaps provide some percentage chance that the house would remain standing. For houses for which there was insufficient structural information to perform an accurate simulation, the system might simply state that the results are inconclusive.
- the results may be used whenever someone submits a search request for houses that could withstand a certain magnitude of earthquake.
- a user might also perform a search request for houses within a certain price range that would not be flooded or destroyed should a category 5 hurricane occur.
- FIG. 25 illustrates a computing system 2500 .
- Computing systems are now increasingly taking a wide variety of forms. Computing systems may, for example, be handheld devices, appliances, laptop computers, desktop computers, mainframes, distributed computing systems, or even devices that have not conventionally been considered a computing system.
- the term “computing system” is defined broadly as including any device or system (or combination thereof) that includes at least one processor, and a memory capable of having thereon computer-executable instructions that may be executed by the processor.
- the memory may take any form and may depend on the nature and form of the computing system.
- a computing system may be distributed over a network environment and may include multiple constituent computing systems.
- a computing system 2500 typically includes at least one processing unit 2502 and memory 2504 .
- the memory 2504 may be physical system memory, which may be volatile, non-volatile, or some combination of the two.
- the term “memory” may also be used herein to refer to non-volatile mass storage such as physical storage media. If the computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
- the term “module” or “component” can refer to software objects or routines that execute on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system (e.g., as separate threads).
- embodiments are described with reference to acts that are performed by one or more computing systems. If such acts are implemented in software, one or more processors of the associated computing system that performs the act direct the operation of the computing system in response to having executed computer-executable instructions.
- An example of such an operation involves the manipulation of data.
- the computer-executable instructions (and the manipulated data) may be stored in the memory 2504 of the computing system 2500 .
- Computing system 2500 may also contain communication channels 2508 that allow the computing system 2500 to communicate with other message processors over, for example, network 2510 .
- Communication channels 2508 are examples of communications media.
- Communications media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information-delivery media.
- communications media include wired media, such as wired networks and direct-wired connections, and wireless media such as acoustic, radio, infrared, and other wireless media.
- the term computer-readable media as used herein includes both storage media and communications media.
- Embodiments within the scope of the present invention also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon.
- Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer.
- Such computer-readable media can comprise physical storage and/or memory media such as RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer.
- Computer-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions.
Abstract
Description
-
- 1) only those homes that experience average noise levels below a certain upper limit;
- 2) only those homes that have a 30 minute commute time or less to the user's work on each of Monday through Thursday,
- 3) only those homes that experience less than a certain threshold of traffic on any road within one fifth of a mile and which are predicted over the next 10 years to remain below that level of traffic,
- 4) only those homes that are not in the shadow of a mountain after 9:15 am at any point in the year,
- 5) only those homes that have sufficient existing trees on the property such that in 10 years time, the trees would shade at least 50% of the area of the roof,
- 6) and so forth.
Such real estate searches are not readily achievable using conventional technology. In each of these examples, the requested home search data may not exist, but the principles described herein may allow the search data for a variety of customized parameters to be generated on the fly as the search is being performed. Also, the search may take advantage of any search data that has been previously solved for. This enables a wide variety of search and exploration capabilities for the user, and opens up a whole new way of exploring a problem to be solved. More regarding the underlying data-driven analytics mechanisms that support these types of searches will now be described.
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